A video can be thought of as a visual document which may be represented from different dimensions such as frames, objects and other different levels of features. Action recognition is usually one of the most important and popular tasks, and requires the understanding of temporal and spatial cues in videos. What structures do the temporal relationships share in common inter- and intra-classes of actions? What is the best representation for those temporal relationships? We propose a new temporal relationship representation, called action graphs based on Laplacian matrices and Allen’s temporal relationships. Recognition framework based on sparse coding, which also mimics human vision system to represent and infer knowledge. To our best knowledge, “action graphs” is put forward to represent the temporal relationships. we are the first using sparse graph coding for event analysis.
CITATION STYLE
Feng, W., Tian, H., Xiao, Y., Ding, J., & Tang, Y. (2017). Action graph decomposition based on sparse coding. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 10666 LNCS, pp. 48–57). Springer Verlag. https://doi.org/10.1007/978-3-319-71607-7_5
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